Stochastic variance reduced optimization for nonconvex sparse learning

Xingguo Li, Tuo Zhao, Raman Arora, Han Liu, Jarvis Haupt

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

We propose a stochastic variance reduced optimization algorithm for solving a class of largescale nonconvex optimization problems with cardinality constraints, and provide sufficient conditions under which the proposed algorithm enjoys strong linear convergence guarantees and optimal estimation accuracy in high dimensions. Numerical experiments demonstrate the efficiency of our method in terms of both parameter estimation and computational performance.

Original languageEnglish (US)
Title of host publication33rd International Conference on Machine Learning, ICML 2016
EditorsMaria Florina Balcan, Kilian Q. Weinberger
PublisherInternational Machine Learning Society (IMLS)
Pages1448-1460
Number of pages13
Volume2
ISBN (Electronic)9781510829008
StatePublished - Jan 1 2016
Event33rd International Conference on Machine Learning, ICML 2016 - New York City, United States
Duration: Jun 19 2016Jun 24 2016

Publication series

Name33rd International Conference on Machine Learning, ICML 2016
Volume2

Conference

Conference33rd International Conference on Machine Learning, ICML 2016
CountryUnited States
CityNew York City
Period6/19/166/24/16

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Computer Networks and Communications

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  • Cite this

    Li, X., Zhao, T., Arora, R., Liu, H., & Haupt, J. (2016). Stochastic variance reduced optimization for nonconvex sparse learning. In M. F. Balcan, & K. Q. Weinberger (Eds.), 33rd International Conference on Machine Learning, ICML 2016 (Vol. 2, pp. 1448-1460). (33rd International Conference on Machine Learning, ICML 2016; Vol. 2). International Machine Learning Society (IMLS).